Browsing by Author "Zawadzka, Joanna"
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Item Open Access Assessing diurnal land surface temperature variations across landcover and local climate zones: implications for urban planning and mitigation strategies on socio-economic factors(Elsevier, 2024-12-01) Palanisamy, Prathiba A.; Zawadzka, Joanna; Jain, Kamal; Bonafoni, Stefania; Tiwari, AnujRising temperatures and rapid urbanization globally reinforce the need to understand urban climates. We investigated the influence of land cover and local climate zones (LCZs) on diurnal land surface temperature (LST) in various seasons in greater Delhi region, India, and their implications on socio-economic factors. Day LST was the highest in the summer and night LST in the monsoon, which also had the lowest diurnal differences in LST. Higher height and density of built-up features contributed to greater heat at night. During the day, open built-up and vegetated areas experienced relatively less heat than their compact equivalents. The lowest diurnal difference was in medium height compact urban zones and tall vegetation. Social inequity in access to urban cooling was indicated by large low-income and heat-vulnerable populations inhabiting the hottest LCZs. This research highlighted that even in semi-arid and subtropical climates, spatial planning policy should consider both the seasonality and diurnal differences in temperature as much as appropriate morphologies for design of thermally comfortable and climate resilient urban spaces. These policies should address the evidenced social inequities in heat exposure to reduce the adverse health impacts on vulnerable groups and therefore contribute to wider societal and economic benefits of healthier populations.Item Open Access Carbon implications of converting cropland to bioenergy crops or forest for climate mitigation: a global assessment(Wiley, 2015-02-06) Albanito, Fabrizio; Beringer, Tim; Corstanje, Ronald; Poulter, Benjamin; Stephenson, Anna; Zawadzka, Joanna; Smith, PeteThe potential for climate change mitigation by bioenergy crops and terrestrial carbon sinks has been the object of intensive research in the past decade. There has been much debate about whether energy crops used to offset fossil fuel use, or carbon sequestration in forests, would provide the best climate mitigation benefit. Most current food cropland is unlikely to be used for bioenergy, but in many regions of the world, a proportion of cropland is being abandoned, particularly marginal croplands, and some of this land is now being used for bioenergy. In this study, we assess the consequences of land-use change on cropland. We first identify areas where cropland is so productive that it may never be converted and assess the potential of the remaining cropland to mitigate climate change by identifying which alternative land use provides the best climate benefit: C4 grass bioenergy crops, coppiced woody energy crops or allowing forest regrowth to create a carbon sink. We do not present this as a scenario of land-use change – we simply assess the best option in any given global location should a land-use change occur. To do this, we use global biomass potential studies based on food crop productivity, forest inventory data and dynamic global vegetation models to provide, for the first time, a global comparison of the climate change implications of either deploying bioenergy crops or allowing forest regeneration on current crop land, over a period of 20 years starting in the nominal year of 2000 ad. Globally, the extent of cropland on which conversion to energy crops or forest would result in a net carbon loss, and therefore likely always to remain as cropland, was estimated to be about 420.1 Mha, or 35.6% of the total cropland in Africa, 40.3% in Asia and Russia Federation, 30.8% in Europe-25, 48.4% in North America, 13.7% in South America and 58.5% in Oceania. Fast growing C4 grasses such as Miscanthus and switch-grass cultivars are the bioenergy feedstock with the highest climate mitigation potential. Fast growing C4 grasses such as Miscanthus and switch-grass cultivars provide the best climate mitigation option on ≈485 Mha of cropland worldwide with ~42% of this land characterized by a terrain slope equal or above 20%. If that land-use change did occur, it would displace ≈58.1 Pg fossil fuel C equivalent (Ceq oil). Woody energy crops such as poplar, willow and Eucalyptus species would be the best option on only 2.4% (≈26.3 Mha) of current cropland, and if this land-use change occurred, it would displace ≈0.9 Pg Ceq oil. Allowing cropland to revert to forest would be the best climate mitigation option on ≈17% of current cropland (≈184.5 Mha), and if this land-use change occurred, it would sequester ≈5.8 Pg C in biomass in the 20-year-old forest and ≈2.7 Pg C in soil. This study is spatially explicit, so also serves to identify the regional differences in the efficacy of different climate mitigation options, informing policymakers developing regionally or nationally appropriate mitigation actions.Item Open Access A datamining approach to identifying spatial patterns of phosphorus forms in the Stormwater Treatment Areas in the Everglades(Elsevier, 2016-10-18) Corstanje, Ronald; Grafius, Darren R.; Zawadzka, Joanna; Moreira Barradas, Joao; Vince, G.; Ivanoff, D.; Pietro, K.The Everglades ecosystem in Florida, USA, is naturally phosphorus (P) limited, and faces threats of ecosystem change and associated losses to habitat, biodiversity, and ecosystem function if subjected to high inflows of P and other nutrients. In addition to changes in historic hydropattern, upstream agriculture (sugar cane, vegetable, citrus) and urbanization has placed the Everglades at risk due to nutrient-rich runoff. In response to this threat, the Stormwater Treatment Areas (STAs) were constructed along the northern boundary of the Everglades as engineered ecological systems designed to retain P from water flowing into the Everglades. This research investigated data collected over a period from 2002 to 2014 from the interior of the STAs using data mining and analysis techniques including (a) exploratory methods such as Principal Component Analysis to test for patterns and groupings in the data, and (b) modelling approaches to test for predictive relationships between environmental variables. The purpose of this research was to reveal and compare spatial trends and relationships between environmental variables across the various treatment cells, flow-ways, and STAs. Common spatial patterns and their drivers indicated that the flow-ways do not function along simple linear gradients; instead forming zonal patterns of P distribution that may increasingly align with the predominant flow path over time. Findings also indicate that the primary drivers of the spatial distribution of P in many of these systems relate to soil characteristics. The results suggest that coupled cycles may be a key component of these systems; i.e. the movement and transformation of P is coupled to that of nitrogen (N).Item Open Access Detection of flood damage in urban residential areas using object-oriented UAV image analysis coupled with tree-based classifiers(MDPI, 2021-09-30) Zawadzka, Joanna; Truckell, Ian; Khouakhi, Abdou; Rivas Casado, MonicaTimely clearing-up interventions are essential for effective recovery of flood-damaged housing, however, time-consuming door-to-door inspections for insurance purposes need to take place before major repairs can be done to adequately assess the losses caused by flooding. With the increased probability of flooding, there is a heightened need for rapid flood damage assessment methods. High resolution imagery captured by unmanned aerial vehicles (UAVs) offers an opportunity for accelerating the time needed for inspections, either through visual interpretation or automated image classification. In this study, object-oriented image segmentation coupled with tree-based classifiers was implemented on a 10 cm resolution RGB orthoimage, captured over the English town of Cockermouth a week after a flood triggered by storm Desmond, to automatically detect debris associated with damages predominantly to residential housing. Random forests algorithm achieved a good level of overall accuracy of 74%, with debris being correctly classified at the rate of 58%, and performing well for small debris (67%) and skips (64%). The method was successful at depicting brightly-colored debris, however, was prone to misclassifications with brightly-colored vehicles. Consequently, in the current stage, the methodology could be used to facilitate visual interpretation of UAV images. Methods to improve accuracy have been identified and discussed.Item Open Access Downscaling Landsat-8 land surface temperature maps in diverse urban landscapes using multivariate adaptive regression splines and very high resolution auxiliary data(Taylor and Francis, 2019-03-25) Zawadzka, Joanna; Corstanje, Ronald; Harris, Jim A.; Truckell, IanWe propose a method for spatial downscaling of Landsat 8-derived LST maps from 100(30 m) resolution down to 2–4 m with the use of the Multiple Adaptive Regression Splines (MARS) models coupled with very high resolution auxiliary data derived from hyperspectral aerial imagery and large-scale topographic maps. We applied the method to four Landsat 8 scenes, two collected in summer and two in winter, for three British towns collectively representing a variety of urban form. We used several spectral indices as well as fractional coverage of water and paved surfaces as LST predictors, and applied a novel method for the correction of temporal mismatch between spectral indices derived from aerial and satellite imagery captured at different dates, allowing for the application of the downscaling method for multiple dates without the need for repeating the aerial survey. Our results suggest that the method performed well for the summer dates, achieving RMSE of 1.40–1.83 K prior to and 0.76–1.21 K after correction for residuals. We conclude that the MARS models, by addressing the non-linear relationship of LST at coarse and fine spatial resolutions, can be successfully applied to produce high resolution LST maps suitable for studies of urban thermal environment at local scales.Item Open Access Ecosystem service multifunctionality and trade-offs in English Green Belt peri-urban planning(Elsevier, 2024-04-17) Kirby, Matthew G.; Zawadzka, Joanna; Scott, Alister J.Green Belt policies govern peri-urban landscapes globally by restricting built development. Yet, they often have little consideration for the land within them. This is especially the case in England where ecosystem services are poorly accounted for in Green Belt policy, whilst also being viewed as a development obstacle, with few environmental and social benefits; a situation mirrored in peri-urban landscapes globally. Moreover, there is a significant research gap into Green Belts through the socio-ecological lenses of ecosystem services and multifunctionality, which allows populist discourses to go unchallenged. Using modelling and participatory mapping data this paper addresses this gap by quantifying the ecosystem service supply, trade-offs and multifunctionality of the North-East Green Belt, and the wider planning and policy implications. The results show that contrary to claims, Green Belts in England can and do provide multiple benefits to people when studied through these lenses. However, levels of individual ecosystem services and overall multifunctionality differ spatially within Green Belts resulting in opportunity areas as well as potential losses of ecosystem services from development. Areas of deciduous and coniferous woodland as well as key “green wedges” close to urban populations were found to be multifunctionality “hots-spots”, whereas arable and improved grassland provide notable “cold-spots”. Trade-offs were mostly from provisioning services. We argue that Green Belt policies explicitly and holistically accounting for ecosystem services could catalyse a multifunctional opportunity space for climate, nature and people in peri-urban landscapes. Additionally, our study demonstrates the conceptual merits of ecosystem service multifunctionality for planning.Item Open Access Ecosystem services from combined natural and engineered water and wastewater treatment - Data accompanying the manuscript published in Ecological Engineering journal(Cranfield University, 2019-05-14 09:28) Zawadzka, Joanna; Gallagher, Elaine; Smith, Heather; Corstanje, RonaldThis data repository contains the spatially explicit results generated during the research described in the manuscript. The methods used to obtain these results are described in detail in the manuscript and supplementary materials associated with the text. The contents of the zipped folders is as follows: - ESS_RBF.zip, ESS_MARSAT.zip, ESS_CW.zip - maps in ESRI shapefile format showing the distribution of the Site/Off-site ratio values across the entire modelling catchments, corresponding to the Figures 4-6 in the manuscript; - Connectivity. zip containing raster files showing the distribution of the resistance values (_resistance.tif) and locations of nodes (_nodes.shp) submitted to the Circuitscape model as well as the model outputs - cumulative current maps (_connectivity.tif). These datasets correspond to Figures 3 and 7 in the manuscript. - Site locations - shapefiles showing the outlines of the three sites assessed in this study. All datasets can be viewed and modified in a GIS software. Input land use/land cover maps for the models cannot be shared due to licence restrictions.Item Open Access Ecosystem services from combined natural and engineered water and wastewater treatment systems: Going beyond water quality enhancement(Elsevier, 2019-05-24) Zawadzka, Joanna; Gallagher, Elaine; Smith, Heather M.; Corstanje, RonaldCombined natural and engineered water and waste water systems (cNES) are nature-based solutions that utilise naturally occurring processes to remove impurities from water and therefore contribute to the ecosystem service of water quality enhancement. We hypothesise that these systems may also have a potential to deliver ecosystem services other than their primary purpose of water purification and we use spatially-explicit modelling tools to determine these benefits. We focused on three different types of cNES: bank filtration (BF), managed aquifer recharge/soil aquifer treatment (MAR/SAT), and constructed wetlands (CW), and combined the ecosystem services cascade, DESSIN and CICES conceptual frameworks with multiple InVEST 3.4.4 models to investigate the spatial distribution of intermediate ecosystem services within the sites as well as in the surrounding landscape. We also determined the role of habitats present within the sites in wider landscape’s connectivity to the nearest Natura 2000 areas using the Circuitscape 4.0 model, assessed the public perception of the aesthetic value of two of the cNES technologies, i.e. CW and MAR/SAT, via an online survey, and linked the determined ecosystem services to their likely beneficiaries. Our results indicated that the sites characterised with semi-natural ecosystems had a good potential for ecosystem services provision and that the selected cNES technologies were favourably received by the public as compared to their engineered equivalents. We concluded that determination of ecosystem services potential from nature-based solutions, such as cNES technologies, should be done in consideration of various contextual factors including the type of habitats/ecosystems present within the proposed solutions, the location within the landscape as well as properties and ecosystem services potential of the areas surrounding the sites, all of which can be facilitated by deployment of spatially-explicit ecosystem service models at early stages of the planning process.Item Open Access Emerging resilience metrics in an intensely managed ecological system(Elsevier, 2024-01-05) Toumasis, Nikolaos; Simms, Daniel; Rust, Will; Harris, Jim A.; White, John R.; Zawadzka, Joanna; Corstanje, RonThere is growing interest in understanding resilience of ecosystems because of the potential of abrupt and possibly irreversible shifts between alternative ecosystem states. Tipping points are observed in systems with strong positive feedback, providing early warning signals of potential instability. These points can be detected through metrics like critical slowing down (CSD), such as increased recovery time, variance, and autocorrelation. These indicators have been tested in laboratory experiments and field settings, ignoring trait changes. Here we present a long-term temporal analysis of several large, intensely monitored constructed wetlands, the Everglades Stormwater Treatment Areas (STAs), in which sudden changes in plant community composition have been observed. Using wavelet analysis, significant increases and decreases of variance properties (long-term flow data, water quality and nutrient TP loads) across these systems can indicate when and which STAs are less resilient to perturbations. In this study, continuous wavelet transform (CWT) was used to determine the periodicity of any cyclical activity in the data and to determine changes in autocorrelation and variance as measures of CSD. The change detection methods were used to find significant changes in variations and correlations across the time series. By employing these techniques, we were able to spot substantial shifts in model-observed wavelet correlation and model residual wavelet variance and thereby identify where these systems exhibit CSD. Although our analysis is limited to historical data, the proposed approach has practical value in that it identifies STAs that may be vulnerable to perturbation. The study also presents one of the few studies in which CSD is observed in practice rather than modelled in theory.Item Open Access Evidence of collaborative opportunities to ensure long-term sustainability in African farming(Elsevier, 2023-02-17) El Fartassi, Imane; Milne, Alice E.; El Alami, Rafiq; Rafiqi, Maryam; Hassall, Kirsty L.; Waine, Toby W.; Zawadzka, Joanna; Diarra, Alhousseine; Corstanje, RonFarmers face the challenge of increasing production to feed a growing population and support livelihoods, whilst also improving the sustainability and resilience of cropping systems. Understanding the key factors that influence farming management practices is crucial for determining farmers' adaptive capacity and willingness to engage in cooperative strategies. To that end, we investigated management practices that farmers adopt and the factors underlying farmers' decision-making. We also aimed to identify the constraints that impede the adoption of strategies perceived to increase farming resilience and to explore how the acceleration of technology adoption through cooperation could ensure the long-term sustainability of farming. Surveys were distributed to farming stakeholders and professionals who worked across the contrasting environments of Morocco. We used descriptive statistics and analysis by log-linear modelling to predict the importance of factors influencing farmers’ decision-making. The results show that influencing factors tended to cluster around environmental pressures, crop characteristics and water availability with social drivers playing a lesser role. Subsidies were also found to be an important factor in decision-making. Farming stakeholders generally believed that collaborative networks are likely to facilitate the adoption of sustainable agricultural practices. We conclude that farmers need both economic incentives and technical support to enhance their adaptive capacity as this can lessen the socioeconomic vulnerability inherent in arid and semi-arid regions.Item Open Access Evidence of ecological critical slowing-down in temperate soils(EGU: European Geophysical Union, 2022-05-27) Fraser, Fiona; Corstanje, Ronald; Todman, Lindsay; Bello-Curás, Diana; Bending, Gary; Deeks, Lynda K.; Harris, Jim A.; Hilton, Sally; Pawlett, Mark; Zawadzka, Joanna; Whitmore, Andrew; Ritz, KarlThe resilience of ecological systems is crucially important, particularly in the context of climate change. We present experimental evidence of critical slowing-down arising from perturbation of a key function in a complex ecosystem, exemplified by soil. Different behavioural classes in soil respiratory patterns were detected in response to repeated drying:rewetting cycles. We characterised these as adaptive, resilient, fragile or non-resilient. The latter involved increasing erratic behaviour (i.e. increasing variance), and the propagation of such behaviour (i.e. autocorrelation), interpreted as a critical slowing-down of the observed function. Soil microbial phenotype and land-use were predominantly related to variance and autocorrelation respectively. No relationship was found between biodiversity and resilience, but the ability of a community to be compositionally flexible rather than biodiversity per se appeared to be key to retaining system function. These data were used to map the extent to which soils are close to crossing into alternative stable states at a national scale.Item Open Access Facilitating the elicitation of beliefs for use in Bayesian Belief modelling(Elsevier, 2019-10-01) Hassall, Kirsty L.; Dailey, Gordon; Zawadzka, Joanna; Milne, Alice E.; Harris, Jim A.; Corstanje, Ronald; Whitmore, Andrew P.Expert opinion is increasingly being used to inform Bayesian Belief Networks, in particular to define the conditional dependencies modelled by the graphical structure. The elicitation of such expert opinion remains a major challenge due to both the quantity of information required and the ability of experts to quantify subjective beliefs effectively. In this work, we introduce a method designed to initialise conditional probability tables based on a small number of simple questions that capture the overall shape of a conditional probability distribution before enabling the expert to refine their results in an efficient way. These methods have been incorporated into a software Application for Conditional probability Elicitation (ACE), freely available at https://github.com/KirstyLHassall/ACE Hassall (2019)Item Open Access The mapping of landscapes, geology and soils of Bedfordshire & Cambridgeshire(2011-12-08T00:00:00Z) Farewell, Timothy S.; Friend, Peter; Whiteley, Martin; Zawadzka, JoannaLandscapes and their component landforms have formed during the long-term geological history of an area, and may have been influenced by many factors. These include, a) the materials present just below the Earth's surface, b) movements of the Earth's land or sea surface, and c) the action of ice, rain, wind and living organisms. This study has been concerned particularly with ways of analysing and presenting topographical information, so that members of the general public can gain new insights into the stories that have resulted in their landscapes and landforms.Item Open Access The need for training and benchmark datasets for convolutional neural networks in flood applications(IWA Publishing, 2022-05-17) Khouakhi, Abdou; Zawadzka, Joanna; Truckell, IanFlood-related image datasets from social media, smartphones, CCTV cameras, and unmanned aerial vehicles (UAVs) present valuable data for the management of flood risk, and particularly for the application of modern convolutional neural networks (CNNs) to specific flood-related problems such as flood extent detection and flood depth estimation. This review discusses the increasing role of CNNs in flood research with a growing number of published datasets, particularly since 2018. We note the lack of open and labelled flood image datasets and the growing need for an open, benchmark data library for image classification, object detection, and segmentation relevant to flood management. Such a library would provide benchmark datasets to advance CNN flood applications in general and serve as a resource, providing data scientists and the flood research community with the necessary data for model training and validation.Item Open Access Operationalizing the ecosystems approach: assessing the environmental impact of major infrastructure development(Elsevier, 2017-03-19) Zawadzka, Joanna; Corstanje, Ronald; Fookes, J.; Nichols, Jonathan; Harris, Jim A.The ecosystem services approach is increasingly applied in the context of environmental resources management and impact assessment. Assessments often involve analysis of alternative scenarios for which potential changes in ecosystem services are quantified. For such assessments to be effective there is a requirement to represent changes in ecosystem services supply in a clear and informative manner. Here we compute Ecosystem Services Ratio (ESR), a simple index that quantifies the relative change in ecosystem service provision under altered conditions given the baseline, and the Modified Ecosystem Services State Index, which averages the ESR scores obtained for each ecosystem service assessed, to provide an overall measure of the change. Given that modelling approaches to quantification of ecosystem services often result in production of maps of ecosystem supply, the proposed metrics can be visualized as maps in support to decision making processes. We use these indices to investigate potential changes in the supply of seven modelled ecosystem services resulting from the introduction of a major road development – a highway with associated green infrastructure – into a predominantly agricultural landscape in the UK. We find that the planted woodland, scrubland and grassland can increase the supply of multiple ecosystem services not accounted for in previous green infrastructure studies, although the magnitude of change differs with the type of vegetation, initial conditions and timeframes of the assessment.Item Open Access A simple method for determination of fine resolution urban form patterns with distinct thermal properties using class-level landscape metrics(Springer, 2020-11-20) Zawadzka, Joanna; Harris, Jim A.; Corstanje, RonaldContext Relationships between land surface temperature (LST) and spatial configuration of urban form described by landscape metrics so far have been investigated with coarse resolution LST imagery within artificially superimposed land divisions. Citywide micro-scale observations are needed to better inform urban design and help mitigate urban heat island effects in warming climates. Objectives The primary objective was to sub-divide an existing high-resolution land cover (LC) map into groups of patches with distinct spatial and thermal properties suitable for urban LST studies relevant to micro-scales. The secondary objective was to provide insights into the optimal analytical unit size to calculate class-level landscape metrics strongly correlated with LST at 2 m spatial resolution. Methods A two-tiered unsupervised k-means clustering analysis was deployed to derive spatially distinct groups of patches of each major LC class followed by further subdivisions into hottest, coldest and intermediary sub-classes, making use of high resolution class-level landscape metrics strongly correlated with LST. Results Aggregation class-level landscape metrics were consistently correlated with LST for green and grey LC classes and the optimal search window size for their calculations was 100 m for LST at 2 m resolution. ANOVA indicated that all Tier 1 and most of Tier 2 subdivisions were thermally and spatially different. Conclusions The two-tiered k-means clustering approach was successful at depicting subdivisions of major LC classes with distinct spatial configuration and thermal properties, especially at a broader Tier 1 level. Further research into spatial configuration of LC patches with similar spatial but different thermal properties is required.Item Open Access Using GIS-linked Bayesian Belief Networks as a tool for modelling urban biodiversity(Elsevier, 2019-05-30) Grafius, Darren R.; Corstanje, Ronald; Warren, Philip H.; Evans, Karl L.; Norton, Briony A.; Siriwardena, Gavin M.; Pescott, Oliver L.; Plummer, Kate E.; Mears, Meghann; Zawadzka, Joanna; Richards, J. Paul; Harris, Jim A.The ability to predict spatial variation in biodiversity is a long-standing but elusive objective of landscape ecology. It depends on a detailed understanding of relationships between landscape and patch structure and taxonomic richness, and accurate spatial modelling. Complex heterogeneous environments such as cities pose particular challenges, as well as heightened relevance, given the increasing rate of urbanisation globally. Here we use a GIS-linked Bayesian Belief Network approach to test whether landscape and patch structural characteristics (including vegetation height, green-space patch size and their connectivity) drive measured taxonomic richness of numerous invertebrate, plant, and avian groups. We find that modelled richness is typically higher in larger and better-connected green-spaces with taller vegetation, indicative of more complex vegetation structure and consistent with the principle of ‘bigger, better, and more joined up’. Assessing the relative importance of these variables indicates that vegetation height is the most influential in determining richness for a majority of taxa. There is variation, however, between taxonomic groups in the relationships between richness and landscape structural characteristics, and the sensitivity of these relationships to particular predictors. Consequently, despite some broad commonalities, there will be trade-offs between different taxonomic groups when designing urban landscapes to maximise biodiversity. This research demonstrates the feasibility of using a GIS-coupled Bayesian Belief Network approach to model biodiversity at fine spatial scales in complex landscapes where current data and appropriate modelling approaches are lacking, and our findings have important implications for ecologists, conservationists and planners.